{"id":23867,"date":"2024-01-09T17:29:56","date_gmt":"2024-01-09T09:29:56","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=23867"},"modified":"2024-01-09T17:29:57","modified_gmt":"2024-01-09T09:29:57","slug":"%e9%ab%98%e5%88%86%e7%94%9f%e4%bf%a1sci-wgcna%e5%88%86%e6%9e%90%e5%a4%8d%e7%8e%b0","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=23867","title":{"rendered":"\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0"},"content":{"rendered":"\n

\u4eca\u5929\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u6765\u7684\u5206\u4eab\u662f\u5565\u5462\uff1f\u4eca\u5929\u5c0f\u679c\u5c06\u590d\u73b0\u4e00\u7bc78+\u751f\u4fe1\u6587\u7ae0\u4e2d\u7684WGCNA\u5206\u6790\uff0c\u901a\u8fc7\u8be5\u63a8\u6587\u5c06\u638c\u63e1\u5982\u4f55\u5229\u7528\u81ea\u5df1\u7684\u6570\u636e\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u6765\u8fdb\u884c\u4e0e\u8868\u578b\u6216\u8005\u75be\u75c5\u76f8\u5173\u5019\u9009\u57fa\u56e0\u7684\u6316\u6398\uff0c\u5c0f\u679c\u89c9\u5f97WGCNA\u662f\u4e00\u4e2a\u975e\u5e38\u4e0d\u9519\u7684\u5019\u9009\u57fa\u56e0\u6316\u6398\u7684\u65b9\u6cd5\uff0c\u503c\u5f97\u5c0f\u4f19\u4f34\u4eec\u8ba4\u771f\u5b66\u4e60\uff0c\u63a5\u4e0b\u6765\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\u5427\uff01<\/p>\n\n\n\n

1.WGCNA\u4ecb\u7ecd<\/p>\n\n\n\n

\u5728\u5f00\u59cb\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u60f3\u4e3a\u5c0f\u4f19\u4f34\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0bWGCNA\uff0cWGCNA\u662f\u4e00\u79cd\u7528\u4e8e\u57fa\u56e0\u5171\u8868\u8fbe\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5c06\u9ad8\u901a\u91cf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8f6c\u5316\u4e3a\u4e00\u4e2a\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u4ece\u800c\u53d1\u73b0\u5177\u6709\u76f8\u4f3c\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\u6a21\u5757\uff0c\u4ee5\u53ca\u7814\u7a76\u8fd9\u4e9b\u6a21\u5757\u4e0e\u8868\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6700\u7ec8\u6316\u6398\u4e0e\u8868\u578b\u76f8\u5173\u7684\u5019\u9009\u57fa\u56e0\u3002\u8fd9\u5c31\u662f\u5c0f\u679c\u5bf9WGCNA\u5206\u6790\u539f\u7406\u7684\u7b80\u5355\u4ecb\u7ecd\uff0c\u662f\u4e0d\u662f\u901a\u4fd7\u6613\u61c2\u5965\uff01\u5176\u5b9eWGCNA\u5206\u6790\u5f88\u7b80\u5355\uff0c\u5c0f\u4f19\u4f34\u4e0d\u8981\u56e0\u4e3a\u5206\u6790\u6b65\u9aa4\u591a\u800c\u4e0d\u6562\u5c1d\u8bd5\uff0c\u53ea\u9700\u8981\u8f93\u5165\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u548c\u5bf9\u5e94\u6837\u672c\u8868\u578b\u6570\u636e\u6587\u4ef6\uff0c\u5c31\u53ef\u4ee5\u5b8c\u6210\u5206\u6790\uff0c\u6709\u9700\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u3002<\/p>\n\n\n\n

2.\u51c6\u5907\u9700\u8981\u7684R\u5305<\/p>\n\n\n\n

#\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/p>\n\n\n\n

BiocManager::install(“WGCNA”)<\/p>\n\n\n\n

#\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/p>\n\n\n\n

library(WGCNA)<\/p>\n\n\n\n

3.WGCNA\u5206\u6790<\/p>\n\n\n\n

#\u8bfb\u53d6\u8868\u8fbe\u77e9\u9635\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\uff0c\u5217\u540d\u4e3a\u6837\u672c\u4fe1\u606f<\/p>\n\n\n\n

expr<-read.table(“combined.expr.txt”,header=T,sep=”\\t”)<\/p>\n\n\n\n

\"Dingtalk_20230613085327\"<\/figure>\n\n\n\n

#\u884c\u5217\u8f6c\u7f6e<\/p>\n\n\n\n

mydata<-expr<\/p>\n\n\n\n

expr2<-data.frame(t(mydata))<\/p>\n\n\n\n

#\u786e\u5b9aexpr2\u5217\u540d<\/p>\n\n\n\n

colnames(expr2)<-rownames(mydata)<\/p>\n\n\n\n

#\u786e\u5b9aexpr2\u884c\u540d<\/p>\n\n\n\n

rownames(expr2)<-colnames(mydata)<\/p>\n\n\n\n

#\u57fa\u56e0\u8fc7\u6ee4<\/p>\n\n\n\n

dataExpr1<-expr2<\/p>\n\n\n\n

gsg=goodSamplesGenes(dataExpr1,verbose=3);<\/p>\n\n\n\n

gsg$allOK<\/p>\n\n\n\n

if (!gsg$allOK){<\/p>\n\n\n\n

# Optionally, print the gene and sample names that were removed:<\/p>\n\n\n\n

if (sum(!gsg$goodGenes)>0)<\/p>\n\n\n\n

printFlush(paste(“Removing genes:”,<\/p>\n\n\n\n

paste(names(dataExpr)[!gsg$goodGenes], collapse = “,”)));<\/p>\n\n\n\n

if (sum(!gsg$goodSamples)>0)<\/p>\n\n\n\n

printFlush(paste(“Removing samples:”,<\/p>\n\n\n\n

paste(rownames(dataExpr)[!gsg$goodSamples], collapse = “,”)));<\/p>\n\n\n\n

# Remove the offending genes and samples from the data:<\/p>\n\n\n\n

dataExpr1 = dataExpr1[gsg$goodSamples, gsg$goodGenes]<\/p>\n\n\n\n

}<\/p>\n\n\n\n

#\u57fa\u56e0\u6570\u76ee<\/p>\n\n\n\n

nGenes = ncol(dataExpr1)<\/p>\n\n\n\n

#\u6837\u672c\u6570\u76ee<\/p>\n\n\n\n

nSamples = nrow(dataExpr1)<\/p>\n\n\n\n

#\u5bfc\u5165\u8868\u578b\u6570\u636e\uff0c\u7b2c\u4e00\u5217\u4e3a\u6837\u672c\u4fe1\u606f\uff0c\u5176\u4ed6\u5217\u4e3a\u8868\u578b\u6570\u636e\uff0c\u9700\u8981\u6ce8\u610f\u7684\u662f\u8868\u578b\u6570\u636e\u8981\u4e0e\u6837\u672c\u6570\u636e\u4e00\u4e00\u5bf9\u5e94<\/p>\n\n\n\n

traitData=read.table(“TraitData.txt”,header=T,sep=”\\t”,row.names=1)<\/p>\n\n\n\n

\"Dingtalk_20230613090611\"<\/figure>\n\n\n\n

#\u63d0\u53d6\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d<\/p>\n\n\n\n

fpkmSamples<-rownames(dataExpr1)<\/p>\n\n\n\n

#\u63d0\u53d6\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/p>\n\n\n\n

traitSamples<-rownames(traitData)<\/p>\n\n\n\n

#\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d\u987a\u5e8f\u5339\u914d\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/p>\n\n\n\n

traitRows<-match(fpkmSamples,traitSamples)<\/p>\n\n\n\n

#\u83b7\u5f97\u5339\u914d\u597d\u6837\u672c\u987a\u5e8f\u7684\u8868\u578b\u6587\u4ef6<\/p>\n\n\n\n

dataTraits<-traitData[traitRows,]<\/p>\n\n\n\n

#\u7ed8\u5236\u6811+\u8868\u578b\u70ed\u56fe<\/p>\n\n\n\n

sampleTree2<-hclust(dist(dataExpr1),method=”average”)<\/p>\n\n\n\n

traitColors<-numbers2colors(dataTraits,signed=FALSE)<\/p>\n\n\n\n

png(“sample-subtype-cluster.png”,width = 800,height = 600)<\/p>\n\n\n\n

plotDendroAndColors(sampleTree2,traitColors,groupLabels=names(dataTraits),main=”Sample dendrogram and trait heatmap”,<\/p>\n\n\n\n

cex.colorLabels=1.5,cex.dendroLabels=1,cex.rowText=2)<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"sample-subtype-cluster\"<\/figure>\n\n\n\n

#\u8ba1\u7b97\u5408\u9002\u7684power\u503c\uff0c\u5e76\u7ed8\u5236Power\u503c\u66f2\u7ebf\u56fe<\/p>\n\n\n\n

powers = c(c(1:10), seq(from = 12, to=20, by=2))<\/p>\n\n\n\n

sft = pickSoftThreshold(dataExpr1, powerVector = powers, verbose = 5)<\/p>\n\n\n\n

#\u8bbe\u7f6e\u7f51\u7edc\u6784\u5efa\u53c2\u6570\u9009\u62e9\u8303\u56f4\uff0c\u8ba1\u7b97\u65e0\u5c3a\u5ea6\u5206\u5e03\u62d3\u6251\u77e9\u9635<\/p>\n\n\n\n

png(“step2-beta-value.png”,width = 800,height = 600)<\/p>\n\n\n\n

par(mfrow = c(1,2));<\/p>\n\n\n\n

cex1 = 0.9;<\/p>\n\n\n\n

# Scale-free topology fit index as a function of the soft-thresholding power<\/p>\n\n\n\n

plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n\n\n\n

xlab=”Soft Threshold (power)”,ylab=”Scale Free Topology Model Fit,signed R^2″,type=”n”,<\/p>\n\n\n\n

main = paste(“Scale independence”));<\/p>\n\n\n\n

text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n\n\n\n

labels=powers,cex=cex1,col=”red”);<\/p>\n\n\n\n

# this line corresponds to using an R^2 cut-off of h<\/p>\n\n\n\n

abline(h=0.90,col=”red”)<\/p>\n\n\n\n

# Mean connectivity as a function of the soft-thresholding power<\/p>\n\n\n\n

plot(sft$fitIndices[,1], sft$fitIndices[,5],<\/p>\n\n\n\n

xlab=”Soft Threshold (power)”,ylab=”Mean Connectivity”, type=”n”,<\/p>\n\n\n\n

main = paste(“Mean connectivity”))<\/p>\n\n\n\n

text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col=”red”)<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step2-beta-value\"<\/figure>\n\n\n\n

#\u4e00\u6b65\u6cd5\u6784\u5efa\u5171\u8868\u8fbe\u7f51\u7edc<\/p>\n\n\n\n

##\u9700\u8981\u8c03\u7528WGCNA\u5305\u81ea\u5e26\u7684cor\u51fd\u6570\uff0c\u4e0d\u7136\u4f1a\u53d1\u751f\u62a5\u9519\u5965\uff01<\/p>\n\n\n\n

cor<-WGCNA::cor<\/p>\n\n\n\n

##\u5728\u8fdb\u884c\u5171\u8868\u8fbe\u7f51\u7edc\u6784\u5efa\u65f6\uff0cpower\u503c\u7684\u9009\u62e9\u975e\u5e38\u91cd\u8981\uff0c\u6700\u5f71\u54cd\u7ed3\u679c\u7684\u4e00\u4e2a\u53c2\u6570\uff0c\u9700\u8981\u7ecf\u8fc7\u591a\u6b21\u5c1d\u8bd5\uff0c\u624d\u80fd\u627e\u5230\u6700\u9002\u5408\u7684\u3002<\/p>\n\n\n\n

net = blockwiseModules(dataExpr1, power = 7, maxBlockSize = nGenes,<\/p>\n\n\n\n

TOMType =’unsigned’, minModuleSize = 30,<\/p>\n\n\n\n

reassignThreshold = 0, mergeCutHeight = 0.25,<\/p>\n\n\n\n

numericLabels = TRUE, pamRespectsDendro = FALSE,<\/p>\n\n\n\n

saveTOMs=TRUE, saveTOMFileBase = “drought”,<\/p>\n\n\n\n

verbose = 3)<\/p>\n\n\n\n

table(net$colors)<\/p>\n\n\n\n

cor<-stats::cor<\/p>\n\n\n\n

#\u7ed8\u5236\u57fa\u56e0\u805a\u7c7b\u6811\u548c\u6a21\u5757\u989c\u8272\u7ec4\u5408<\/p>\n\n\n\n

# Convert labels to colors for plotting<\/p>\n\n\n\n

moduleLabels = net$colors<\/p>\n\n\n\n

moduleColors = labels2colors(moduleLabels)<\/p>\n\n\n\n

# Plot the dendrogram and the module colors underneath<\/p>\n\n\n\n

png(“step4-genes-modules.png”,width = 800,height = 600)<\/p>\n\n\n\n

plotDendroAndColors(net$dendrograms[[1]], moduleColors[net$blockGenes[[1]]],<\/p>\n\n\n\n

“Module colors”,<\/p>\n\n\n\n

dendroLabels = FALSE, hang = 0.03,<\/p>\n\n\n\n

addGuide = TRUE, guideHang = 0.05)<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step4-genes-modules\"<\/figure>\n\n\n\n

#\u8ba1\u7b97\u6a21\u5757\u4e0e\u6027\u72b6\u95f4\u7684\u76f8\u5173\u6027\u53ca\u7ed8\u5236\u76f8\u5173\u6027\u70ed\u56fe<\/p>\n\n\n\n

nGenes = ncol(dataExpr1)<\/p>\n\n\n\n

nSamples = nrow(dataExpr1)<\/p>\n\n\n\n

design=read.table(“TraitData.txt”, sep = ‘\\t’, header = T, row.names = 1)<\/p>\n\n\n\n

# Recalculate MEs with color labels<\/p>\n\n\n\n

MEs0 = moduleEigengenes(dataExpr1, moduleColors)$eigengenes<\/p>\n\n\n\n

##\u4e0d\u540c\u989c\u8272\u7684\u6a21\u5757\u7684ME\u503c\u77e9 (\u6837\u672cvs\u6a21\u5757)<\/p>\n\n\n\n

MEs = orderMEs(MEs0);<\/p>\n\n\n\n

moduleTraitCor = cor(MEs, design , use = “p”);<\/p>\n\n\n\n

moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)<\/p>\n\n\n\n

# Will display correlations and their p-values<\/p>\n\n\n\n

textMatrix = paste(signif(moduleTraitCor, 2), “\\n(“,<\/p>\n\n\n\n

signif(moduleTraitPvalue, 1), “)”, sep = “”);<\/p>\n\n\n\n

dim(textMatrix) = dim(moduleTraitCor)<\/p>\n\n\n\n

png(“step5-Module-trait-relationships.png”,width = 800,height = 1200,res = 120)<\/p>\n\n\n\n

par(mar = c(6, 8.5, 3, 3));<\/p>\n\n\n\n

# Display the correlation values within a heatmap plot<\/p>\n\n\n\n

labeledHeatmap(Matrix = moduleTraitCor,<\/p>\n\n\n\n

xLabels = colnames(design),<\/p>\n\n\n\n

yLabels = names(MEs),<\/p>\n\n\n\n

ySymbols = names(MEs),<\/p>\n\n\n\n

colorLabels = FALSE,<\/p>\n\n\n\n

colors = greenWhiteRed(50),<\/p>\n\n\n\n

textMatrix = textMatrix,<\/p>\n\n\n\n

setStdMargins = FALSE,<\/p>\n\n\n\n

cex.text = 0.5,<\/p>\n\n\n\n

zlim = c(-1,1),<\/p>\n\n\n\n

main = paste(“Module-trait relationships”))<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step5-Module-trait-relationships\"<\/figure>\n\n\n\n

#\u8ba1\u7b97MM\u503c\u548cGS\u503c\u5e76\u7ed8\u56fe<\/p>\n\n\n\n

##\u5207\u5272\uff0c\u4ece\u7b2c\u4e09\u4e2a\u5b57\u7b26\u5f00\u59cb\u4fdd\u5b58<\/p>\n\n\n\n

modNames = substring(names(MEs), 3)<\/p>\n\n\n\n

geneModuleMembership = as.data.frame(cor(dataExpr1, MEs, use = “p”));<\/p>\n\n\n\n

## \u7b97\u51fa\u6bcf\u4e2a\u6a21\u5757\u8ddf\u57fa\u56e0\u7684\u76ae\u5c14\u68ee\u76f8\u5173\u7cfb\u6570\u77e9<\/p>\n\n\n\n

## MEs\u662f\u6bcf\u4e2a\u6a21\u5757\u5728\u6bcf\u4e2a\u6837\u672c\u91cc\u9762\u76bf<\/p>\n\n\n\n

## dataExpr1\u662f\u6bcf\u4e2a\u57fa\u56e0\u5728\u6bcf\u4e2a\u6837\u672c\u7684\u8868\u8fbe\u91cf<\/p>\n\n\n\n

MMPvalue = as.data.frame(<\/p>\n\n\n\n

corPvalueStudent(as.matrix(geneModuleMembership), nSamples) ##\u8ba1\u7b97MM\u503c\u5bf9\u5e94\u7684P\u503c<\/p>\n\n\n\n

);<\/p>\n\n\n\n

names(geneModuleMembership) = paste(“MM”, modNames, sep=””); ##\u7ed9MM\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/p>\n\n\n\n

names(MMPvalue) = paste(“p.MM”, modNames, sep=””); ##\u7ed9MMPvalue\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/p>\n\n\n\n

##\u8ba1\u7b97\u57fa\u56e0\u4e0e\u6bcf\u4e2a\u6027\u72b6\u7684\u663e\u8457\u6027\uff08\u76f8\u5173\u6027\uff09\u53capvalue\u503c<\/p>\n\n\n\n

geneTraitSignificance = as.data.frame(cor(dataExpr1, design, use = “p”));<\/p>\n\n\n\n

GSPvalue = as.data.frame(<\/p>\n\n\n\n

corPvalueStudent(as.matrix(geneTraitSignificance), nSamples) ##\u8ba1\u7b97GS\u503c\u5bf9\u5e94\u7684pvalue\u503c<\/p>\n\n\n\n

);<\/p>\n\n\n\n

names(geneTraitSignificance) = paste(“GS.”, colnames(design), sep=””);<\/p>\n\n\n\n

names(GSPvalue) = paste(“p.GS.”, colnames(design), sep=””);<\/p>\n\n\n\n

module = “brown”<\/p>\n\n\n\n

column = match(module, modNames); ##\u5728\u6240\u6709\u6a21\u5757\u4e2d\u5339\u914d\u9009\u62e9\u7684\u6a21\u5757\uff0c\u8fd4\u56de\u6240\u5728\u7684\u4f4d\u7f6e<\/p>\n\n\n\n

moduleGenes = moduleColors==module; ##==\u5339\u914d\uff0c\u5339\u914d\u4e0a\u7684\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56defalse<\/p>\n\n\n\n

png(“step6-Module_membership-gene_significance.png”,width = 800,height = 600)<\/p>\n\n\n\n

par(mfrow = c(1,1)); ##\u8bbe\u5b9a\u8f93\u51fa\u56fe\u7247\u884c\u5217\u6570\u91cf\uff0c\uff081\uff3f1\uff09\u4ee3\u8868\u884c\u4e00\u4e2a\uff0c\u5217\u4e00\u4e3f<\/p>\n\n\n\n

verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]), ## \u7ed8\u5236MM\u548cGS\u6563\u70b9\u56ff<\/p>\n\n\n\n

abs(geneTraitSignificance[moduleGenes, 1]),<\/p>\n\n\n\n

xlab = paste(“Module Membership in”, module, “module”),<\/p>\n\n\n\n

ylab = “Gene significance for Basal”,<\/p>\n\n\n\n

main = paste(“Module membership vs. gene significance\\n”),<\/p>\n\n\n\n

cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step6-Module_membership-gene_significance\"<\/figure>\n\n\n\n

#\u7ed8\u5236TOM\u70ed\u56fe+\u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/p>\n\n\n\n

geneTree = net$dendrograms[[1]]; ##\u63d0\u53d6\u57fa\u56e0\u805a\u7c7b<\/p>\n\n\n\n

dissTOM = 1-TOMsimilarityFromExpr(dataExpr1, power = 7); ##\u8ba1\u7b97TOM\u8ddd\u79bb<\/p>\n\n\n\n

plotTOM = dissTOM^7; ##\u901a\u8fc77\u6b21\u65b9\u5904\u7406\u8fdb\u884c\u6807\u51c6\u5316\uff0c\u964d\u4f4e\u57fa\u56e0\u95f4\u7684\u8bef\u5dee<\/p>\n\n\n\n

diag(plotTOM) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/p>\n\n\n\n

TOMplot(plotTOM, geneTree, moduleColors, main = “Network heatmap plot, all genes”)<\/p>\n\n\n\n

nSelect = 400 ##\u6311\u9009\u9700\u8981\u63d0\u53d6\u7684\u57fa\u56e0<\/p>\n\n\n\n

set.seed(10); ##\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u6570\uff0c\u4fdd\u8bc1\u9009\u53d6\u7684\u968f\u673a\u6027<\/p>\n\n\n\n

select = sample(nGenes, size = nSelect); ##\u4eff5000\u4e2a\u57fa\u56e0\u4e2d\u968f\u673a\u53ff400\u4e3f<\/p>\n\n\n\n

selectTOM = dissTOM[select, select]; ##\u63d0\u53d6\u6311\u51fa\u6765\u7684\u57fa\u56e0\u7684TOM\u8ddd\u79bb<\/p>\n\n\n\n

# There\u2019s no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.<\/p>\n\n\n\n

selectTree = hclust(as.dist(selectTOM), method = “average”) ##\u5bf9\u6311\u51fa\u6765\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb\u91cd\u65b0\u805a\u7c7b<\/p>\n\n\n\n

selectColors = moduleColors[select];<\/p>\n\n\n\n

# Open a graphical window<\/p>\n\n\n\n

sizeGrWindow(9,9)<\/p>\n\n\n\n

# Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing<\/p>\n\n\n\n

# the color palette; setting the diagonal to NA also improves the clarity of the plot<\/p>\n\n\n\n

plotDiss = selectTOM^7; ##\u5bf9\u6311\u9009\u51fa\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb7\u6b21\u65b9\u5904\u7406<\/p>\n\n\n\n

diag(plotDiss) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/p>\n\n\n\n

png(“step7-Network-heatmap.png”,width = 800,height = 600)<\/p>\n\n\n\n

TOMplot(plotDiss, selectTree, selectColors, ##\u7ed8\u5236TOM\u56fe<\/p>\n\n\n\n

main = “Network heatmap plot, selected genes”)<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step7-Network-heatmap\"<\/figure>\n\n\n\n

# \u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/p>\n\n\n\n

RA = as.data.frame(design[,2]); ##\u63d0\u53d6\u7279\u5b9a\u6027\u72b6<\/p>\n\n\n\n

names(RA) = “RA”<\/p>\n\n\n\n

# Add the weight to existing module eigengenes<\/p>\n\n\n\n

MET = orderMEs(cbind(MEs, RA))<\/p>\n\n\n\n

# Plot the relationships among the eigengenes and the trait<\/p>\n\n\n\n

sizeGrWindow(5,7.5);<\/p>\n\n\n\n

par(cex = 0.9)<\/p>\n\n\n\n

png(“step7-Eigengene-dendrogram.png”,width = 800,height = 600)<\/p>\n\n\n\n

plotEigengeneNetworks(MET, “”, ##\u7ed8\u5236\u6a21\u5757\u805a\u7c7b\u56fe\u548c\u70ed\u56fe<\/p>\n\n\n\n

marDendro =c(0,5,1,5), ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/p>\n\n\n\n

marHeatmap = c(5,6,1,2), cex.lab = 0.8, ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/p>\n\n\n\n

xLabelsAngle = 90) ##\u8f74\u9c9c\u827f90\u5ebf<\/p>\n\n\n\n

dev.off()<\/p>\n\n\n\n

\"step7-Eigengene-dendrogram\"<\/figure>\n\n\n\n

#\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684\u7f51\u7edc\u7ed3\u679c<\/p>\n\n\n\n

TOM = TOMsimilarityFromExpr(dataExpr1, power = 7); ##\u9700\u8981\u8f83\u957f\u65f6\u95f4<\/p>\n\n\n\n

# \u9009\u62e9brown\u6a21\u5757<\/p>\n\n\n\n

module = “brown”;<\/p>\n\n\n\n

# Select module probes<\/p>\n\n\n\n

probes = colnames(dataExpr1)<\/p>\n\n\n\n

inModule = (moduleColors==module);<\/p>\n\n\n\n

## \u63d0\u53d6\u6307\u5b9a\u6a21\u5757\u7684\u57fa\u56e0\u540d<\/p>\n\n\n\n

modProbes = probes[inModule];<\/p>\n\n\n\n

\"Dingtalk_20230613103051\"<\/figure>\n\n\n\n

modTOM = TOM[inModule, inModule];<\/p>\n\n\n\n

dimnames(modTOM) = list(modProbes, modProbes)<\/p>\n\n\n\n

## \u6a21\u5757\u5bf9\u5e94\u7684\u57fa\u56e0\u5173\u7cfb\u77e9<\/p>\n\n\n\n

cyt = exportNetworkToCytoscape( ##\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684Cytoscape\u8f93\u5165\u6587\u4ef6<\/p>\n\n\n\n

modTOM,<\/p>\n\n\n\n

edgeFile = paste(“CytoscapeInput-edges-“, paste(module, collapse=”-“), “.txt”, sep=””), ##\u8f93\u51fa\u8fb9\u6587\u4ef6\u540d<\/p>\n\n\n\n

nodeFile = paste(“CytoscapeInput-nodes-“, paste(module, collapse=”-“), “.txt”, sep=””), ##\u8f93\u51fa\u70b9\u6587\u4ef6\u540d<\/p>\n\n\n\n

weighted = TRUE,<\/p>\n\n\n\n

threshold = 0.02,<\/p>\n\n\n\n

nodeNames = modProbes,<\/p>\n\n\n\n

nodeAttr = moduleColors[inModule]<\/p>\n\n\n\n

)<\/p>\n\n\n\n

CytoscapeInput-edges-brown.txt\uff0c\u8fb9\u6587\u4ef6<\/p>\n\n\n\n

\"Dingtalk_20230613102320\"<\/figure>\n\n\n\n

CytoscapeInput-nodes-brown.txt,\u70b9\u6587\u4ef6<\/p>\n\n\n\n

\"Dingtalk_20230613102430\"<\/figure>\n\n\n\n

\u6700\u7ec8\u5c0f\u679c\u6210\u529f\u5b8c\u6210\u4e86WGCNA\u5206\u6790\uff0c\u57fa\u672c\u8fbe\u5230\u4e86\u590d\u73b0\uff0c\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u5566\uff01\u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u5e73\u53f0\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u975e\u5e38\u65b9\u4fbf\u5965\uff0c\u4e91\u5e73\u53f0\u7f51\u5740\u4e3a\uff1ahttp:\/\/www.biocloudservice.com\/home.html,\u5305\u62ec\u4e86WGCNA\u5206\u6790(http:\/\/www.biocloudservice.com\/336\/336.php),GEO\u6570\u636e\u4e0b\u8f7d(http:\/\/www.biocloudservice.com\/371\/371.php)\u7b49\u5c0f\u5de5\u5177\uff0c\u6b22\u8fce\u5927\u5bb6\u548c\u5c0f\u679c\u4e00\u8d77\u8ba8\u8bba\u5b66\u4e60\u54c8\uff01\uff01\uff01\uff01<\/p>\n\n\n

\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0<\/p>\n

\u4eca\u5929\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u6765\u7684\u5206\u4eab\u662f\u5565\u5462\uff1f\u4eca\u5929\u5c0f\u679c\u5c06\u590d\u73b0\u4e00\u7bc78+\u751f\u4fe1\u6587\u7ae0\u4e2d\u7684WGCNA\u5206\u6790\uff0c\u901a\u8fc7\u8be5\u63a8\u6587\u5c06\u638c\u63e1\u5982\u4f55\u5229\u7528\u81ea\u5df1\u7684\u6570\u636e\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u6765\u8fdb\u884c\u4e0e\u8868\u578b\u6216\u8005\u75be\u75c5\u76f8\u5173\u5019\u9009\u57fa\u56e0\u7684\u6316\u6398\uff0c\u5c0f\u679c\u89c9\u5f97WGCNA\u662f\u4e00\u4e2a\u975e\u5e38\u4e0d\u9519\u7684\u5019\u9009\u57fa\u56e0\u6316\u6398\u7684\u65b9\u6cd5\uff0c\u503c\u5f97\u5c0f\u4f19\u4f34\u4eec\u8ba4\u771f\u5b66\u4e60\uff0c\u63a5\u4e0b\u6765\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\u5427\uff01<\/p>\n

1.WGCNA\u4ecb\u7ecd<\/p>\n

\u5728\u5f00\u59cb\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u60f3\u4e3a\u5c0f\u4f19\u4f34\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0bWGCNA\uff0cWGCNA\u662f\u4e00\u79cd\u7528\u4e8e\u57fa\u56e0\u5171\u8868\u8fbe\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5c06\u9ad8\u901a\u91cf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8f6c\u5316\u4e3a\u4e00\u4e2a\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u4ece\u800c\u53d1\u73b0\u5177\u6709\u76f8\u4f3c\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\u6a21\u5757\uff0c\u4ee5\u53ca\u7814\u7a76\u8fd9\u4e9b\u6a21\u5757\u4e0e\u8868\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6700\u7ec8\u6316\u6398\u4e0e\u8868\u578b\u76f8\u5173\u7684\u5019\u9009\u57fa\u56e0\u3002\u8fd9\u5c31\u662f\u5c0f\u679c\u5bf9WGCNA\u5206\u6790\u539f\u7406\u7684\u7b80\u5355\u4ecb\u7ecd\uff0c\u662f\u4e0d\u662f\u901a\u4fd7\u6613\u61c2\u5965\uff01\u5176\u5b9eWGCNA\u5206\u6790\u5f88\u7b80\u5355\uff0c\u5c0f\u4f19\u4f34\u4e0d\u8981\u56e0\u4e3a\u5206\u6790\u6b65\u9aa4\u591a\u800c\u4e0d\u6562\u5c1d\u8bd5\uff0c\u53ea\u9700\u8981\u8f93\u5165\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u548c\u5bf9\u5e94\u6837\u672c\u8868\u578b\u6570\u636e\u6587\u4ef6\uff0c\u5c31\u53ef\u4ee5\u5b8c\u6210\u5206\u6790\uff0c\u6709\u9700\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u3002<\/p>\n

2.\u51c6\u5907\u9700\u8981\u7684R\u5305<\/p>\n

#\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/p>\n

BiocManager::install(“WGCNA”)<\/p>\n

#\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/p>\n

library(WGCNA)<\/p>\n

3.WGCNA\u5206\u6790<\/p>\n

#\u8bfb\u53d6\u8868\u8fbe\u77e9\u9635\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\uff0c\u5217\u540d\u4e3a\u6837\u672c\u4fe1\u606f<\/p>\n

expr<-read.table(“combined.expr.txt”,header=T,sep=”\\t”)<\/p>\n

\"Dingtalk_20230613085327\"<\/p>\n

#\u884c\u5217\u8f6c\u7f6e<\/p>\n

mydata<-expr<\/p>\n

expr2<-data.frame(t(mydata))<\/p>\n

#\u786e\u5b9aexpr2\u5217\u540d<\/p>\n

colnames(expr2)<-rownames(mydata)<\/p>\n

#\u786e\u5b9aexpr2\u884c\u540d<\/p>\n

rownames(expr2)<-colnames(mydata)<\/p>\n

#\u57fa\u56e0\u8fc7\u6ee4<\/p>\n

dataExpr1<-expr2<\/p>\n

gsg=goodSamplesGenes(dataExpr1,verbose=3);<\/p>\n

gsg$allOK<\/p>\n

if (!gsg$allOK){<\/p>\n

# Optionally, print the gene and sample names that were removed:<\/p>\n

if (sum(!gsg$goodGenes)>0)<\/p>\n

printFlush(paste(“Removing genes:”,<\/p>\n

paste(names(dataExpr)[!gsg$goodGenes], collapse = “,”)));<\/p>\n

if (sum(!gsg$goodSamples)>0)<\/p>\n

printFlush(paste(“Removing samples:”,<\/p>\n

paste(rownames(dataExpr)[!gsg$goodSamples], collapse = “,”)));<\/p>\n

# Remove the offending genes and samples from the data:<\/p>\n

dataExpr1 = dataExpr1[gsg$goodSamples, gsg$goodGenes]<\/p>\n

}<\/p>\n

#\u57fa\u56e0\u6570\u76ee<\/p>\n

nGenes = ncol(dataExpr1)<\/p>\n

#\u6837\u672c\u6570\u76ee<\/p>\n

nSamples = nrow(dataExpr1)<\/p>\n

#\u5bfc\u5165\u8868\u578b\u6570\u636e\uff0c\u7b2c\u4e00\u5217\u4e3a\u6837\u672c\u4fe1\u606f\uff0c\u5176\u4ed6\u5217\u4e3a\u8868\u578b\u6570\u636e\uff0c\u9700\u8981\u6ce8\u610f\u7684\u662f\u8868\u578b\u6570\u636e\u8981\u4e0e\u6837\u672c\u6570\u636e\u4e00\u4e00\u5bf9\u5e94<\/p>\n

traitData=read.table(“TraitData.txt”,header=T,sep=”\\t”,row.names=1)<\/p>\n

\"Dingtalk_20230613090611\"<\/p>\n

#\u63d0\u53d6\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d<\/p>\n

fpkmSamples<-rownames(dataExpr1)<\/p>\n

#\u63d0\u53d6\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/p>\n

traitSamples<-rownames(traitData)<\/p>\n

#\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d\u987a\u5e8f\u5339\u914d\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/p>\n

traitRows<-match(fpkmSamples,traitSamples)<\/p>\n

#\u83b7\u5f97\u5339\u914d\u597d\u6837\u672c\u987a\u5e8f\u7684\u8868\u578b\u6587\u4ef6<\/p>\n

dataTraits<-traitData[traitRows,]<\/p>\n

#\u7ed8\u5236\u6811+\u8868\u578b\u70ed\u56fe<\/p>\n

sampleTree2<-hclust(dist(dataExpr1),method=”average”)<\/p>\n

traitColors<-numbers2colors(dataTraits,signed=FALSE)<\/p>\n

png(“sample-subtype-cluster.png”,width = 800,height = 600)<\/p>\n

plotDendroAndColors(sampleTree2,traitColors,groupLabels=names(dataTraits),main=”Sample dendrogram and trait heatmap”,<\/p>\n

cex.colorLabels=1.5,cex.dendroLabels=1,cex.rowText=2)<\/p>\n

dev.off()<\/p>\n

\"sample-subtype-cluster\"<\/p>\n

#\u8ba1\u7b97\u5408\u9002\u7684power\u503c\uff0c\u5e76\u7ed8\u5236Power\u503c\u66f2\u7ebf\u56fe<\/p>\n

powers = c(c(1:10), seq(from = 12, to=20, by=2))<\/p>\n

sft = pickSoftThreshold(dataExpr1, powerVector = powers, verbose = 5)<\/p>\n

#\u8bbe\u7f6e\u7f51\u7edc\u6784\u5efa\u53c2\u6570\u9009\u62e9\u8303\u56f4\uff0c\u8ba1\u7b97\u65e0\u5c3a\u5ea6\u5206\u5e03\u62d3\u6251\u77e9\u9635<\/p>\n

png(“step2-beta-value.png”,width = 800,height = 600)<\/p>\n

par(mfrow = c(1,2));<\/p>\n

cex1 = 0.9;<\/p>\n

# Scale-free topology fit index as a function of the soft-thresholding power<\/p>\n

plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n

xlab=”Soft Threshold (power)”,ylab=”Scale Free Topology Model Fit,signed R^2″,type=”n”,<\/p>\n

main = paste(“Scale independence”));<\/p>\n

text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n

labels=powers,cex=cex1,col=”red”);<\/p>\n

# this line corresponds to using an R^2 cut-off of h<\/p>\n

abline(h=0.90,col=”red”)<\/p>\n

# Mean connectivity as a function of the soft-thresholding power<\/p>\n

plot(sft$fitIndices[,1], sft$fitIndices[,5],<\/p>\n

xlab=”Soft Threshold (power)”,ylab=”Mean Connectivity”, type=”n”,<\/p>\n

main = paste(“Mean connectivity”))<\/p>\n

text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col=”red”)<\/p>\n

dev.off()<\/p>\n

\"step2-beta-value\"<\/p>\n

#\u4e00\u6b65\u6cd5\u6784\u5efa\u5171\u8868\u8fbe\u7f51\u7edc<\/p>\n

##\u9700\u8981\u8c03\u7528WGCNA\u5305\u81ea\u5e26\u7684cor\u51fd\u6570\uff0c\u4e0d\u7136\u4f1a\u53d1\u751f\u62a5\u9519\u5965\uff01<\/p>\n

cor<-WGCNA::cor<\/p>\n

##\u5728\u8fdb\u884c\u5171\u8868\u8fbe\u7f51\u7edc\u6784\u5efa\u65f6\uff0cpower\u503c\u7684\u9009\u62e9\u975e\u5e38\u91cd\u8981\uff0c\u6700\u5f71\u54cd\u7ed3\u679c\u7684\u4e00\u4e2a\u53c2\u6570\uff0c\u9700\u8981\u7ecf\u8fc7\u591a\u6b21\u5c1d\u8bd5\uff0c\u624d\u80fd\u627e\u5230\u6700\u9002\u5408\u7684\u3002<\/p>\n

net = blockwiseModules(dataExpr1, power = 7, maxBlockSize = nGenes,<\/p>\n

TOMType =’unsigned’, minModuleSize = 30,<\/p>\n

reassignThreshold = 0, mergeCutHeight = 0.25,<\/p>\n

numericLabels = TRUE, pamRespectsDendro = FALSE,<\/p>\n

saveTOMs=TRUE, saveTOMFileBase = “drought”,<\/p>\n

verbose = 3)<\/p>\n

table(net$colors)<\/p>\n

cor<-stats::cor<\/p>\n

#\u7ed8\u5236\u57fa\u56e0\u805a\u7c7b\u6811\u548c\u6a21\u5757\u989c\u8272\u7ec4\u5408<\/p>\n

# Convert labels to colors for plotting<\/p>\n

moduleLabels = net$colors<\/p>\n

moduleColors = labels2colors(moduleLabels)<\/p>\n

# Plot the dendrogram and the module colors underneath<\/p>\n

png(“step4-genes-modules.png”,width = 800,height = 600)<\/p>\n

plotDendroAndColors(net$dendrograms[[1]], moduleColors[net$blockGenes[[1]]],<\/p>\n

“Module colors”,<\/p>\n

dendroLabels = FALSE, hang = 0.03,<\/p>\n

addGuide = TRUE, guideHang = 0.05)<\/p>\n

dev.off()<\/p>\n

\"step4-genes-modules\"<\/p>\n

#\u8ba1\u7b97\u6a21\u5757\u4e0e\u6027\u72b6\u95f4\u7684\u76f8\u5173\u6027\u53ca\u7ed8\u5236\u76f8\u5173\u6027\u70ed\u56fe<\/p>\n

nGenes = ncol(dataExpr1)<\/p>\n

nSamples = nrow(dataExpr1)<\/p>\n

design=read.table(“TraitData.txt”, sep = ‘\\t’, header = T, row.names = 1)<\/p>\n

# Recalculate MEs with color labels<\/p>\n

MEs0 = moduleEigengenes(dataExpr1, moduleColors)$eigengenes<\/p>\n

##\u4e0d\u540c\u989c\u8272\u7684\u6a21\u5757\u7684ME\u503c\u77e9 (\u6837\u672cvs\u6a21\u5757)<\/p>\n

MEs = orderMEs(MEs0);<\/p>\n

moduleTraitCor = cor(MEs, design , use = “p”);<\/p>\n

moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)<\/p>\n

# Will display correlations and their p-values<\/p>\n

textMatrix = paste(signif(moduleTraitCor, 2), “\\n(“,<\/p>\n

signif(moduleTraitPvalue, 1), “)”, sep = “”);<\/p>\n

dim(textMatrix) = dim(moduleTraitCor)<\/p>\n

png(“step5-Module-trait-relationships.png”,width = 800,height = 1200,res = 120)<\/p>\n

par(mar = c(6, 8.5, 3, 3));<\/p>\n

# Display the correlation values within a heatmap plot<\/p>\n

labeledHeatmap(Matrix = moduleTraitCor,<\/p>\n

xLabels = colnames(design),<\/p>\n

yLabels = names(MEs),<\/p>\n

ySymbols = names(MEs),<\/p>\n

colorLabels = FALSE,<\/p>\n

colors = greenWhiteRed(50),<\/p>\n

textMatrix = textMatrix,<\/p>\n

setStdMargins = FALSE,<\/p>\n

cex.text = 0.5,<\/p>\n

zlim = c(-1,1),<\/p>\n

main = paste(“Module-trait relationships”))<\/p>\n

dev.off()<\/p>\n

\"step5-Module-trait-relationships\"<\/p>\n

#\u8ba1\u7b97MM\u503c\u548cGS\u503c\u5e76\u7ed8\u56fe<\/p>\n

##\u5207\u5272\uff0c\u4ece\u7b2c\u4e09\u4e2a\u5b57\u7b26\u5f00\u59cb\u4fdd\u5b58<\/p>\n

modNames = substring(names(MEs), 3)<\/p>\n

geneModuleMembership = as.data.frame(cor(dataExpr1, MEs, use = “p”));<\/p>\n

## \u7b97\u51fa\u6bcf\u4e2a\u6a21\u5757\u8ddf\u57fa\u56e0\u7684\u76ae\u5c14\u68ee\u76f8\u5173\u7cfb\u6570\u77e9<\/p>\n

## MEs\u662f\u6bcf\u4e2a\u6a21\u5757\u5728\u6bcf\u4e2a\u6837\u672c\u91cc\u9762\u76bf<\/p>\n

## dataExpr1\u662f\u6bcf\u4e2a\u57fa\u56e0\u5728\u6bcf\u4e2a\u6837\u672c\u7684\u8868\u8fbe\u91cf<\/p>\n

MMPvalue = as.data.frame(<\/p>\n

corPvalueStudent(as.matrix(geneModuleMembership), nSamples) ##\u8ba1\u7b97MM\u503c\u5bf9\u5e94\u7684P\u503c<\/p>\n

);<\/p>\n

names(geneModuleMembership) = paste(“MM”, modNames, sep=””); ##\u7ed9MM\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/p>\n

names(MMPvalue) = paste(“p.MM”, modNames, sep=””); ##\u7ed9MMPvalue\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/p>\n

##\u8ba1\u7b97\u57fa\u56e0\u4e0e\u6bcf\u4e2a\u6027\u72b6\u7684\u663e\u8457\u6027\uff08\u76f8\u5173\u6027\uff09\u53capvalue\u503c<\/p>\n

geneTraitSignificance = as.data.frame(cor(dataExpr1, design, use = “p”));<\/p>\n

GSPvalue = as.data.frame(<\/p>\n

corPvalueStudent(as.matrix(geneTraitSignificance), nSamples) ##\u8ba1\u7b97GS\u503c\u5bf9\u5e94\u7684pvalue\u503c<\/p>\n

);<\/p>\n

names(geneTraitSignificance) = paste(“GS.”, colnames(design), sep=””);<\/p>\n

names(GSPvalue) = paste(“p.GS.”, colnames(design), sep=””);<\/p>\n

module = “brown”<\/p>\n

column = match(module, modNames); ##\u5728\u6240\u6709\u6a21\u5757\u4e2d\u5339\u914d\u9009\u62e9\u7684\u6a21\u5757\uff0c\u8fd4\u56de\u6240\u5728\u7684\u4f4d\u7f6e<\/p>\n

moduleGenes = moduleColors==module; ##==\u5339\u914d\uff0c\u5339\u914d\u4e0a\u7684\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56defalse<\/p>\n

png(“step6-Module_membership-gene_significance.png”,width = 800,height = 600)<\/p>\n

par(mfrow = c(1,1)); ##\u8bbe\u5b9a\u8f93\u51fa\u56fe\u7247\u884c\u5217\u6570\u91cf\uff0c\uff081\uff3f1\uff09\u4ee3\u8868\u884c\u4e00\u4e2a\uff0c\u5217\u4e00\u4e3f<\/p>\n

verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]), ## \u7ed8\u5236MM\u548cGS\u6563\u70b9\u56ff<\/p>\n

abs(geneTraitSignificance[moduleGenes, 1]),<\/p>\n

xlab = paste(“Module Membership in”, module, “module”),<\/p>\n

ylab = “Gene significance for Basal”,<\/p>\n

main = paste(“Module membership vs. gene significance\\n”),<\/p>\n

cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)<\/p>\n

dev.off()<\/p>\n

\"step6-Module_membership-gene_significance\"<\/p>\n

#\u7ed8\u5236TOM\u70ed\u56fe+\u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/p>\n

geneTree = net$dendrograms[[1]]; ##\u63d0\u53d6\u57fa\u56e0\u805a\u7c7b<\/p>\n

dissTOM = 1-TOMsimilarityFromExpr(dataExpr1, power = 7); ##\u8ba1\u7b97TOM\u8ddd\u79bb<\/p>\n

plotTOM = dissTOM^7; ##\u901a\u8fc77\u6b21\u65b9\u5904\u7406\u8fdb\u884c\u6807\u51c6\u5316\uff0c\u964d\u4f4e\u57fa\u56e0\u95f4\u7684\u8bef\u5dee<\/p>\n

diag(plotTOM) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/p>\n

TOMplot(plotTOM, geneTree, moduleColors, main = “Network heatmap plot, all genes”)<\/p>\n

nSelect = 400 ##\u6311\u9009\u9700\u8981\u63d0\u53d6\u7684\u57fa\u56e0<\/p>\n

set.seed(10); ##\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u6570\uff0c\u4fdd\u8bc1\u9009\u53d6\u7684\u968f\u673a\u6027<\/p>\n

select = sample(nGenes, size = nSelect); ##\u4eff5000\u4e2a\u57fa\u56e0\u4e2d\u968f\u673a\u53ff400\u4e3f<\/p>\n

selectTOM = dissTOM[select, select]; ##\u63d0\u53d6\u6311\u51fa\u6765\u7684\u57fa\u56e0\u7684TOM\u8ddd\u79bb<\/p>\n

# There\u2019s no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.<\/p>\n

selectTree = hclust(as.dist(selectTOM), method = “average”) ##\u5bf9\u6311\u51fa\u6765\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb\u91cd\u65b0\u805a\u7c7b<\/p>\n

selectColors = moduleColors[select];<\/p>\n

# Open a graphical window<\/p>\n

sizeGrWindow(9,9)<\/p>\n

# Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing<\/p>\n

# the color palette; setting the diagonal to NA also improves the clarity of the plot<\/p>\n

plotDiss = selectTOM^7; ##\u5bf9\u6311\u9009\u51fa\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb7\u6b21\u65b9\u5904\u7406<\/p>\n

diag(plotDiss) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/p>\n

png(“step7-Network-heatmap.png”,width = 800,height = 600)<\/p>\n

TOMplot(plotDiss, selectTree, selectColors, ##\u7ed8\u5236TOM\u56fe<\/p>\n

main = “Network heatmap plot, selected genes”)<\/p>\n

dev.off()<\/p>\n

\"step7-Network-heatmap\"<\/p>\n

# \u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/p>\n

RA = as.data.frame(design[,2]); ##\u63d0\u53d6\u7279\u5b9a\u6027\u72b6<\/p>\n

names(RA) = “RA”<\/p>\n

# Add the weight to existing module eigengenes<\/p>\n

MET = orderMEs(cbind(MEs, RA))<\/p>\n

# Plot the relationships among the eigengenes and the trait<\/p>\n

sizeGrWindow(5,7.5);<\/p>\n

par(cex = 0.9)<\/p>\n

png(“step7-Eigengene-dendrogram.png”,width = 800,height = 600)<\/p>\n

plotEigengeneNetworks(MET, “”, ##\u7ed8\u5236\u6a21\u5757\u805a\u7c7b\u56fe\u548c\u70ed\u56fe<\/p>\n

marDendro =c(0,5,1,5), ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/p>\n

marHeatmap = c(5,6,1,2), cex.lab = 0.8, ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/p>\n

xLabelsAngle = 90) ##\u8f74\u9c9c\u827f90\u5ebf<\/p>\n

dev.off()<\/p>\n

\"step7-Eigengene-dendrogram\"<\/p>\n

#\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684\u7f51\u7edc\u7ed3\u679c<\/p>\n

TOM = TOMsimilarityFromExpr(dataExpr1, power = 7); ##\u9700\u8981\u8f83\u957f\u65f6\u95f4<\/p>\n

# \u9009\u62e9brown\u6a21\u5757<\/p>\n

module = “brown”;<\/p>\n

# Select module probes<\/p>\n

probes = colnames(dataExpr1)<\/p>\n

inModule = (moduleColors==module);<\/p>\n

## \u63d0\u53d6\u6307\u5b9a\u6a21\u5757\u7684\u57fa\u56e0\u540d<\/p>\n

modProbes = probes[inModule];<\/p>\n

\"Dingtalk_20230613103051\"<\/p>\n

modTOM = TOM[inModule, inModule];<\/p>\n

dimnames(modTOM) = list(modProbes, modProbes)<\/p>\n

## \u6a21\u5757\u5bf9\u5e94\u7684\u57fa\u56e0\u5173\u7cfb\u77e9<\/p>\n

cyt = exportNetworkToCytoscape( ##\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684Cytoscape\u8f93\u5165\u6587\u4ef6<\/p>\n

modTOM,<\/p>\n

edgeFile = paste(“CytoscapeInput-edges-“, paste(module, collapse=”-“), “.txt”, sep=””), ##\u8f93\u51fa\u8fb9\u6587\u4ef6\u540d<\/p>\n

nodeFile = paste(“CytoscapeInput-nodes-“, paste(module, collapse=”-“), “.txt”, sep=””), ##\u8f93\u51fa\u70b9\u6587\u4ef6\u540d<\/p>\n

weighted = TRUE,<\/p>\n

threshold = 0.02,<\/p>\n

nodeNames = modProbes,<\/p>\n

nodeAttr = moduleColors[inModule]<\/p>\n

)<\/p>\n

CytoscapeInput-edges-brown.txt\uff0c\u8fb9\u6587\u4ef6<\/p>\n

\"Dingtalk_20230613102320\"<\/p>\n

CytoscapeInput-nodes-brown.txt,\u70b9\u6587\u4ef6<\/p>\n

\"Dingtalk_20230613102430\"<\/p>\n

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